Here’s Why Natural Language Processing is the Future of BI

Every time you ask Siri for directions, a complex chain of cutting edge code is activated. It allows ‘her’ to understand your question, find the information you’re looking for, and respond to you in a language that you understand. This has only become possible in the last few years. Until now, we have been interacting with computers in a way that they understand, rather than us. We have learned their language.

But now they’re learning ours.

The technology underpinning this revolution in human-computer relations is Natural Language Processing (NLP). And it’s already transforming BI, in ways that go far beyond simply making the interface easier. Before long, business transforming, life changing information will be discovered merely by talking with a chatbot.

This future is not far away. In some ways, it’s already here.

What is Natural Language Processing?

NLP, otherwise known as computational linguistics, is the combination of machine learning, AI, and linguistics that allows us to talk to machines as if they were human.

Think about how until a few years ago, effective Google searching was achieved by using exactly the right keywords structured with Boolean search terms: AND, OR and NOT. To get the answers you wanted out of Google, you had to know its language.

Then Google introduced semantic search. Its algorithm learned associations between words, enabling you to ask it a question the same way you would a friend. Internally, it translated that question into a Boolean structured search that it understood – but the process was invisible.

It’s the same technology that allows you to ask Siri what the weather is today or what the cheapest flight to Borneo is tomorrow, without modifying your English into computational logic gates.

Simply ask ‘what’s the cheapest flight to Borneo tomorrow?’ and Siri “understands”; trawling airlines for flights from your location to Borneo, comparing cost parameters to find the lowest. Siri understands “tomorrow” and “cheapest” without you having to specify the date, or define cheapest as the lowest price.

These examples are relatively nascent. Although impressive, they can still be frustratingly hit and miss, and when they hit it’s because your question can be answered with highly structured data. But NLP aims to eventually render GUIs – even UIs – obsolete, so that interacting with a machine is as easy as talking to a human.

NLP will democratize data

The biggest consequence will be the lowering, or complete removal, of the barrier to entry for BI and big data in general. Many companies in the BI space are taking notice of this trend and making strides to ensure data is becoming more and more user-friendly and easily accessible. But there’s still a way to go.

Imagine, for example; you’re able to get answers to important questions anytime, anywhere, just by asking a question. By turning BI into a conversation with a chatbot, accessing information will be as easy as asking – ‘how have revenues changed over the last three-quarters?’ – rather than needing years of experience, and familiarity with the software, to understand how to ask the question to get the data you need.

It will also make it easier to access on the go as the need for a GUI is removed. Queries can be made by text or voice command on smartphones; the processing is taken care of in the cloud.

Google might tell you today what the weather will be tomorrow. But soon enough, you’ll be able to ask your personal data chatbot about customer sentiment today, and how they’ll feel about your brand next week; all while walking down the street.

NLP will make BI more insightful

Currently, NLP tends to be based on turning natural language into machine language. But as the technology matures – especially the AI component – the computer will get better at “understanding” the query and start to deliver answers rather than search results.

This is one step further from asking the question in natural language. It’s receiving it that way too. Initially, the data chatbot will probably take a question like ‘how have revenues changed over the last three-quarters?’ and return pages of data for you to analyze.

But once it learns the semantic relations and inferences of the question, it will be able to automatically perform the filtering and organization necessary to provide an intelligible answer, rather than simply showing you data.

You won’t just ask the question in natural language. You’ll receive a natural language answer.

NLP will harness unstructured data

NLP expands the scope of what that answer may rest on, by making unstructured data understandable to a machine.

Early attempts at sentiment analysis already go beyond detecting when, for example, a tweet is about your business, to analyzing the surrounding text and determining whether that tweet is positive, negative or neutral. As speech recognition improves, audio and video will become increasingly accessible sources as well.

It’s still early days. Think of today’s sentiment analysis as the sort of accuracy you get when using Google Translate to decode a German news article (a process that relies heavily on aspects of NLP itself.) IBM’s Watson – at the forefront of sentiment analysis – can currently only detect joy, fear, sadness, disgust and anger. Humans feel many more emotions than these.

Well, most do.

But as Watson & Co become more nuanced, NLP opens up the enormous troves of publicly available multimedia for mass analysis by machine – taking data that previously required a human eye to interpret and spitting out either quantitative answers, natural language answers, or both.

Hiring a personal data assistant

The interface by which all this will be achieved is similar to the customer service chat-bots you see on websites today.

Depending on the program, it may straddle your other applications, integrating your BI analytics into every aspect of your business, offering data-driven analytics at every point of the day.

Imagine your BI chatbot hovering in the corner, like the old Word paperclip (but good), ready to answer questions within a Slack chat, Skype meeting or in preparation for an event in your Outlook Calendar.

Rather than asking yourself ‘who was my best sales performer this year?’ and clicking through your data to find the answer, you’ll be able to simply ask the chatbot that question, and it will give you the answer as easily as if you were asking a friend on Whatsapp.

Data available on tap, in any context, on any device. In plain English.